"""simple docstring"""



import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
   import torch

   from transformers import (
       MraForMaskedLM,
       MraForMultipleChoice,
       MraForQuestionAnswering,
       MraForSequenceClassification,
       MraForTokenClassification,
       MraModel,
   )
   from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST






class  snake_case__      :


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=2    ,		lowerCamelCase=8    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=99    ,		lowerCamelCase=16    ,		lowerCamelCase=5    ,		lowerCamelCase=2    ,		lowerCamelCase=36    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.0    ,		lowerCamelCase=512    ,		lowerCamelCase=16    ,		lowerCamelCase=2    ,		lowerCamelCase=0.02    ,		lowerCamelCase=3    ,		lowerCamelCase=4    ,		lowerCamelCase=None    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    seq_length
        __a       =    is_training
        __a       =    use_input_mask
        __a       =    use_token_type_ids
        __a       =    use_labels
        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    num_hidden_layers
        __a       =    num_attention_heads
        __a       =    intermediate_size
        __a       =    hidden_act
        __a       =    hidden_dropout_prob
        __a       =    attention_probs_dropout_prob
        __a       =    max_position_embeddings
        __a       =    type_vocab_size
        __a       =    type_sequence_label_size
        __a       =    initializer_range
        __a       =    num_labels
        __a       =    num_choices
        __a       =    scope


 def 						a__   ( self						):
        __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size						)

        __a       =    None
        if self.use_input_mask:
               __a       =    random_attention_mask([self.batch_size, self.seq_length]						)

        __a       =    None
        if self.use_token_type_ids:
               __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.type_vocab_size						)

        __a       =    None
        __a       =    None
        __a       =    None
        if self.use_labels:
               __a       =    ids_tensor([self.batch_size]    ,		self.type_sequence_label_size						)
               __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.num_labels						)
               __a       =    ids_tensor([self.batch_size]    ,		self.num_choices						)

        __a       =    self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


 def 						a__   ( self						):
        return MraConfig(
            vocab_size=self.vocab_size    ,		hidden_size=self.hidden_size    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		hidden_act=self.hidden_act    ,		hidden_dropout_prob=self.hidden_dropout_prob    ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		type_vocab_size=self.type_vocab_size    ,		is_decoder=lowerCamelCase    ,		initializer_range=self.initializer_range    ,		)


 def 						a__   ( self						):
        __a       =    self.get_config()
        __a       =    300
        return config


 def 						a__   ( self						):
        (
            (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   
        )       =    self.prepare_config_and_inputs()

        __a       =    True
        __a       =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
        __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		vocab_size=2						)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    MraModel(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		token_type_ids=lowerCamelCase						)
        __a       =    model(lowerCamelCase    ,		token_type_ids=lowerCamelCase						)
        __a       =    model(lowerCamelCase						)
        self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		):
        __a       =    True
        __a       =    MraModel(lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(
            lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		encoder_hidden_states=lowerCamelCase    ,		encoder_attention_mask=lowerCamelCase    ,		)
        __a       =    model(
            lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		encoder_hidden_states=lowerCamelCase    ,		)
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		token_type_ids=lowerCamelCase						)
        self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    MraForMaskedLM(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		labels=lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    MraForQuestionAnswering(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(
            lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		start_positions=lowerCamelCase    ,		end_positions=lowerCamelCase    ,		)
        self.parent.assertEqual(result.start_logits.shape    ,		(self.batch_size, self.seq_length)						)
        self.parent.assertEqual(result.end_logits.shape    ,		(self.batch_size, self.seq_length)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_labels
        __a       =    MraForSequenceClassification(lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		labels=lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_labels
        __a       =    MraForTokenClassification(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		labels=lowerCamelCase						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.num_labels)						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_choices
        __a       =    MraForMultipleChoice(config=lowerCamelCase						)
        model.to(lowerCamelCase						)
        model.eval()
        __a       =    input_ids.unsqueeze(1						).expand(-1    ,		self.num_choices    ,		-1						).contiguous()
        __a       =    token_type_ids.unsqueeze(1						).expand(-1    ,		self.num_choices    ,		-1						).contiguous()
        __a       =    input_mask.unsqueeze(1						).expand(-1    ,		self.num_choices    ,		-1						).contiguous()
        __a       =    model(
            lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		labels=lowerCamelCase    ,		)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_choices)						)






 def 						a__   ( self						):
        __a       =    self.prepare_config_and_inputs()
        (
            (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   (
            __a
        ) ,   
        )       =    config_and_inputs
        __a       =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict






@require_torch
class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					str							  =						(
     (
         MraModel,
         MraForMaskedLM,
         MraForMultipleChoice,
         MraForQuestionAnswering,
         MraForSequenceClassification,
         MraForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 _snake_case       :					Optional[Any]							  =						False
 _snake_case       :					int							  =						False
 _snake_case       :					Optional[Any]							  =						False
 _snake_case       :					str							  =						False

 _snake_case       :					Union[str, Any]							  =						()


 def 						a__   ( self						):
        __a       =    MraModelTester(self						)
        __a       =    ConfigTester(self    ,		config_class=lowerCamelCase    ,		hidden_size=37						)


 def 						a__   ( self						):
        self.config_tester.run_common_tests()


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
               __a       =    type
               self.model_tester.create_and_check_model(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCamelCase						)


 @slow
 def 						a__   ( self						):
        for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a       =    MraModel.from_pretrained(lowerCamelCase						)
               self.assertIsNotNone(lowerCamelCase						)






 @unittest.skip(reason="MRA does not output attentions"						)
 def 						a__   ( self						):
        return






@require_torch
class  snake_case__      (       unittest.TestCase       ):


 @slow
 def 						a__   ( self						):
        __a       =    MraModel.from_pretrained("uw-madison/mra-base-512-4"						)
        __a       =    torch.arange(256						).unsqueeze(0						)

        with torch.no_grad():
               __a       =    model(lowerCamelCase						)[0]

        __a       =    torch.Size((1, 256, 768)						)
        self.assertEqual(output.shape    ,		lowerCamelCase						)

        __a       =    torch.tensor(
            [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]						)

        self.assertTrue(torch.allclose(output[:, :3, :3]    ,		lowerCamelCase    ,		atol=1E-4						)						)


 @slow
 def 						a__   ( self						):
        __a       =    MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"						)
        __a       =    torch.arange(256						).unsqueeze(0						)

        with torch.no_grad():
               __a       =    model(lowerCamelCase						)[0]

        __a       =    50265

        __a       =    torch.Size((1, 256, vocab_size)						)
        self.assertEqual(output.shape    ,		lowerCamelCase						)

        __a       =    torch.tensor(
            [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]						)

        self.assertTrue(torch.allclose(output[:, :3, :3]    ,		lowerCamelCase    ,		atol=1E-4						)						)






 @slow
 def 						a__   ( self						):
        __a       =    MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"						)
        __a       =    torch.arange(4096						).unsqueeze(0						)

        with torch.no_grad():
               __a       =    model(lowerCamelCase						)[0]

        __a       =    50265

        __a       =    torch.Size((1, 4096, vocab_size)						)
        self.assertEqual(output.shape    ,		lowerCamelCase						)

        __a       =    torch.tensor(
            [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]						)

        self.assertTrue(torch.allclose(output[:, :3, :3]    ,		lowerCamelCase    ,		atol=1E-4						)						)

"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Union[str, Any]							  =						"""sew-d"""


 def __init__( self    ,		lowerCamelCase=32    ,		lowerCamelCase=768    ,		lowerCamelCase=12    ,		lowerCamelCase=12    ,		lowerCamelCase=3072    ,		lowerCamelCase=2    ,		lowerCamelCase=512    ,		lowerCamelCase=256    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=("p2c", "c2p")    ,		lowerCamelCase="layer_norm"    ,		lowerCamelCase="gelu_python"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.02    ,		lowerCamelCase=1E-7    ,		lowerCamelCase=1E-5    ,		lowerCamelCase="group"    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,		lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,		lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,		lowerCamelCase=False    ,		lowerCamelCase=128    ,		lowerCamelCase=16    ,		lowerCamelCase=True    ,		lowerCamelCase=0.05    ,		lowerCamelCase=10    ,		lowerCamelCase=2    ,		lowerCamelCase=0.0    ,		lowerCamelCase=10    ,		lowerCamelCase=0    ,		lowerCamelCase="mean"    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=256    ,		lowerCamelCase=0    ,		lowerCamelCase=1    ,		lowerCamelCase=2    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase    ,		pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase						)
        __a       =    hidden_size
        __a       =    feat_extract_norm
        __a       =    feat_extract_activation
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    conv_bias
        __a       =    num_conv_pos_embeddings
        __a       =    num_conv_pos_embedding_groups
        __a       =    len(self.conv_dim						)
        __a       =    num_hidden_layers
        __a       =    intermediate_size
        __a       =    squeeze_factor
        __a       =    max_position_embeddings
        __a       =    position_buckets
        __a       =    share_att_key
        __a       =    relative_attention
        __a       =    norm_rel_ebd
        __a       =    list(lowerCamelCase						)
        __a       =    hidden_act
        __a       =    num_attention_heads
        __a       =    hidden_dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    feat_proj_dropout
        __a       =    final_dropout
        __a       =    layer_norm_eps
        __a       =    feature_layer_norm_eps
        __a       =    initializer_range
        __a       =    vocab_size

        if (
            (len(self.conv_stride						) != self.num_feat_extract_layers)
            or (len(self.conv_kernel						) != self.num_feat_extract_layers)
            or (len(self.conv_dim						) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a       =    apply_spec_augment
        __a       =    mask_time_prob
        __a       =    mask_time_length
        __a       =    mask_time_min_masks
        __a       =    mask_feature_prob
        __a       =    mask_feature_length
        __a       =    mask_feature_min_masks

        # ctc loss
        __a       =    ctc_loss_reduction
        __a       =    ctc_zero_infinity

        # sequence classification
        __a       =    use_weighted_layer_sum
        __a       =    classifier_proj_size






 @property
 def 						a__   ( self						):
        return functools.reduce(operator.mul    ,		self.conv_stride    ,		1						)
